Experience is thought to play a critical role in shaping the cortical representations that support object recognition by creating neural responses are selective for some dimensions of change and invariant to others. Although many previous studies have examined the effects of supervised training on object selective regions of the brain, much less is known about the degree to which statistical regularities in the retinal input can directly shape the neural substrates involved in object recognition. Unsupervised learning is important because it allows the brain to employ simple self organizing mechanisms that turn the continuous flux of visual input into the stable objects of our experience. While behavioral and computational work strongly suggests that unsupervised learning plays a key role in object recognition, most related neuroscience work examining the role of input statistics has focused on its effects in early visual areas. Here we propose experiments that combine cutting edge techniques in fMRI, psychophysics, and computational modeling to examine two hypotheses concerning unsupervised learning in object recognition. First, we propose that neural responses may become tuned to match the range and frequency of shape and object exemplars experienced during unsupervised training. That is, neural responses will increase and become more selective for items seen more frequently during unsupervised training relative to infrequently seen or untrained items. This may provide a mechanism which improves discrimination performance for stimuli seen most frequently. Second, behavioral and computational evidence suggests the intriguing hypothesis that the brain uses spatio-temporal correlations as a means for binding different images as belonging to the same object, allowing for recognition of the same object across dramatic transformations, such as changes in its appearance due to rotation. We will determine if spatio- temporal correlations in the visual input during unsupervised training increases the invariance of both brain responses and perceptual performance relative to similar items trained in an uncorrelated manner and pre- training responses (and performance). Third, we will examine if mechanisms of unsupervised learning generalize to supervised learning. In all of our experiments we will examine neural responses and performance both before and after unsupervised training, and use computational modeling to link fMRI data to the possible underlying neural mechanisms such as sharpening of neural tuning and increased firing rates. The proposed work will fill important gaps in knowledge by providing the first account of the neural mechanisms that generate effective representations for object recognition from the statistics of visual experience.